Leveraging Polygenic Enrichments of Gene Features to Predict Genes Underlying Complex Traits and Diseases

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding…

AI identifies key gene sets that cause complex diseases - Scientific Inquirer

Northwestern University biophysicists have developed a new computational tool for identifying the gene combinations underlying complex illnesses like diabetes, cancer and asthma. Unlike single-gene disorders, these conditions are influenced by a network of multiple genes working together. But the sheer number of possible gene combinations is huge, making it incredibly difficult for researchers to pinpoint the specific ones that cause disease. Us…